Author: Maldonado, J.
Paper Title Page
WEPV024 X-Ray Beamline Control with Machine Learning and an Online Model 695
  • B. Nash, D.T. Abell, D.L. Bruhwiler, E.G. Carlin, J.P. Edelen, M.V. Keilman, P. Moeller, R. Nagler, I.V. Pogorelov, S.D. Webb
    RadiaSoft LLC, Boulder, Colorado, USA
  • Y. Du, A. Giles, J. Lynch, J. Maldonado, M.S. Rakitin, A. Walter
    BNL, Upton, New York, USA
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract DE-SC0020593.
We present recent developments on control of x-ray beamlines for synchrotron light sources. Effective models of the x-ray transport are updated based on diagnostics data, and take the form of simplified physics models as well as learned models from scanning over mirror and slit configurations. We are developing this approach to beamline control in collaboration with several beamlines at the NSLS-II. By connecting our online models to the Blue-Sky framework, we enable a convenient interface between the operating machine and the model that may be applied to beamlines at multiple facilities involved in this collaborative software development.
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About • Received ※ 10 October 2021       Accepted ※ 21 November 2021       Issue date ※ 17 December 2021  
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